This is a follow up post to an earlier post on calculation of hog feature vectors for object detection using opencv. Here I describe how a support vector machine (svm) can be trained for a dataset containing positive and negative examples of the object to detected. The code has been commented for easier understanding of how it works :

/*This function takes in a the path and names of
64x128 pixel images, the size of the cell to be
used for calculation of hog features(which should
be 8x8 pixels, some modifications will have to be
done in the code for a different cell size, which
could be easily done once the reader understands
how the code works), a default block size of 2x2
cells has been considered and the window size
parameter should be 64x128 pixels (appropriate
modifications can be easily done for other say
64x80 pixel window size). All the training images
are expected to be stored at the same location and
the names of all the images are expected to be in
sequential order like a1.jpg, a2.jpg, a3.jpg ..
and so on or a(1).jpg, a(2).jpg, a(3).jpg ... The
explanation of all the parameters below will make
clear the usage of the function. The synopsis of
the function is as follows :
prefix : it should be the path of the images, along
with the prefix in the image name for
example if the present working directory is
/home/saurabh/hog/ and the images are in
/home/saurabh/hog/images/positive/ and are
named like pos1.jpg, pos2.jpg, pos3.jpg ....,
then the prefix parameter would be
"images/positive/pos" or if the images are
named like pos(1).jpg, pos(2).jpg,
pos(3).jpg ... instead, the prefix parameter
would be "images/positive/pos("
suffix : it is the part of the name of the image
files after the number for example for the
above examples it would be ".jpg" or ").jpg"
cell : it should be CvSize(8,8), appropriate changes
need to be made for other cell sizes
window : it should be CvSize(64,128), appropriate
changes need to be made for other window sizes
number_samples : it should be equal to the number of
training images, for example if the
training images are pos1.jpg, pos2.jpg
..... pos1216.jpg, then it should be
1216
start_index : it should be the start index of the images'
names for example for the above case it
should be 1 or if the images were named
like pos1000.jpg, pos1001.jpg, pos1002.jpg
.... pos2216.jpg, then it should be 1000
end_index : it should be the end index of the images'
name for example for the above cases it
should be 1216 or 2216
savexml : if you want to store the extracted features,
then you can pass to it the name of an xml
file to which they should be saved
normalization : the normalization scheme to be used for
computing the hog features, any of the
opencv schemes could be passed or -1
could be passed if no normalization is
to be done */
CvMat *train_64x128(char *prefix, char *suffix, CvSize cell,
CvSize window, int number_samples, int start_index,
int end_index, char *savexml = NULL, int canny = 0,
int block = 1, int normalization = 4)
{
char filename[50] = "\0", number[8];
int prefix_length;
prefix_length = strlen(prefix);
int bins = 9;
/* A default block size of 2x2 cells is considered */
int block_width = 2, block_height = 2;
/* Calculation of the length of a feature vector for
an image (64x128 pixels)*/
int feature_vector_length;
feature_vector_length = (((window.width -
cell.width * block_width) / cell.width) +
1) *
(((window.height - cell.height * block_height)
/ cell.height) + 1) * 36;
/* Matrix to store the feature vectors for
all(number_samples) the training samples */
CvMat *training = cvCreateMat(number_samples,
feature_vector_length, CV_32FC1);
CvMat row;
CvMat *img_feature_vector;
IplImage **integrals;
int i = 0, j = 0;
printf("Beginning to extract HoG features from
positive images\n");
strcat(filename, prefix);
/* Loop to calculate hog features for each image one by one */
for (i = start_index; i <= end_index; i++) {
cvtInt(number, i);
strcat(filename, number);
strcat(filename, suffix);
IplImage *img = cvLoadImage(filename);
/* Calculation of the integral histogram for
fast calculation of hog features*/
integrals = calculateIntegralHOG(img);
cvGetRow(training, &row, j);
img_feature_vector
= calculateHOG_window(integrals, cvRect(0, 0,
window.width,
window.height),
normalization);
cvCopy(img_feature_vector, &row);
j++;
printf("%s\n", filename);
filename[prefix_length] = '\0';
for (int k = 0; k < 9; k++) {
cvReleaseImage(&integrals[k]);
}
}
if (savexml != NULL) {
cvSave(savexml, training);
}
return training;
}
/* This function is almost the same as
train_64x128(...), except the fact that it can
take as input images of bigger sizes and
generate multiple samples out of a single
image.
It takes 2 more parameters than
train_64x128(...), horizontal_scans and
vertical_scans to determine how many samples
are to be generated from the image. It
generates horizontal_scans x vertical_scans
number of samples. The meaning of rest of the
parameters is same.
For example for a window size of
64x128 pixels, if a 320x240 pixel image is
given input with horizontal_scans = 5 and
vertical scans = 2, then it will generate to
samples by considering windows in the image
with (x,y,width,height) as (0,0,64,128),
(64,0,64,128), (128,0,64,128), .....,
(0,112,64,128), (64,112,64,128) .....
(256,112,64,128)
The function takes non-overlapping windows
from the image except the last row and last
column, which could overlap with the second
last row or second last column. So the values
of horizontal_scans and vertical_scans passed
should be such that it is possible to perform
that many scans in a non-overlapping fashion
on the given image. For example horizontal_scans
= 5 and vertical_scans = 3 cannot be passed for
a 320x240 pixel image as that many vertical scans
are not possible for an image of height 240
pixels and window of height 128 pixels. */
CvMat *train_large(char *prefix, char *suffix,
CvSize cell, CvSize window, int number_images,
int horizontal_scans, int vertical_scans,
int start_index, int end_index,
char *savexml = NULL, int normalization = 4)
{
char filename[50] = "\0", number[8];
int prefix_length;
prefix_length = strlen(prefix);
int bins = 9;
/* A default block size of 2x2 cells is considered */
int block_width = 2, block_height = 2;
/* Calculation of the length of a feature vector for
an image (64x128 pixels)*/
int feature_vector_length;
feature_vector_length = (((window.width -
cell.width * block_width) / cell.width) +
1) *
(((window.height - cell.height * block_height)
/ cell.height) + 1) * 36;
/* Matrix to store the feature vectors for
all(number_samples) the training samples */
CvMat *training = cvCreateMat(number_images
* horizontal_scans * vertical_scans,
feature_vector_length, CV_32FC1);
CvMat row;
CvMat *img_feature_vector;
IplImage **integrals;
int i = 0, j = 0;
strcat(filename, prefix);
printf("Beginning to extract HoG features
from negative images\n");
/* Loop to calculate hog features for each
image one by one */
for (i = start_index; i <= end_index; i++) {
cvtInt(number, i);
strcat(filename, number);
strcat(filename, suffix);
IplImage *img = cvLoadImage(filename);
integrals = calculateIntegralHOG(img);
for (int l = 0; l < vertical_scans - 1; l++) {
for (int k = 0; k < horizontal_scans - 1; k++) {
cvGetRow(training, &row, j);
img_feature_vector =
calculateHOG_window(integrals,
cvRect(window.width * k,
window.height *
l, window.width,
window.height),
normalization);
cvCopy(img_feature_vector, &row);
j++;
}
cvGetRow(training, &row, j);
img_feature_vector =
calculateHOG_window(integrals,
cvRect(img->width -
window.width,
window.height * l,
window.width,
window.height),
normalization);
cvCopy(img_feature_vector, &row);
j++;
}
for (int k = 0; k < horizontal_scans - 1; k++) {
cvGetRow(training, &row, j);
img_feature_vector =
calculateHOG_window(integrals,
cvRect(window.width * k,
img->height -
window.height,
window.width,
window.height),
normalization);
cvCopy(img_feature_vector, &row);
j++;
}
cvGetRow(training, &row, j);
img_feature_vector = calculateHOG_window(integrals,
cvRect(img->width -
window.width,
img->height -
window.height,
window.width,
window.height),
normalization);
cvCopy(img_feature_vector, &row);
j++;
printf("%s\n", filename);
filename[prefix_length] = '\0';
for (int k = 0; k < 9; k++) {
cvReleaseImage(&integrals[k]);
}
cvReleaseImage(&img);
}
printf("%d negative samples created \n", training->rows);
if (savexml != NULL) {
cvSave(savexml, training);
printf("Negative samples saved as %s\n", savexml);
}
return training;
}
/* This function trains a linear support vector
machine for object classification. The synopsis is
as follows :
pos_mat : pointer to CvMat containing hog feature
vectors for positive samples. This may be
NULL if the feature vectors are to be read
from an xml file
neg_mat : pointer to CvMat containing hog feature
vectors for negative samples. This may be
NULL if the feature vectors are to be read
from an xml file
savexml : The name of the xml file to which the learnt
svm model should be saved
pos_file: The name of the xml file from which feature
vectors for positive samples are to be read.
It may be NULL if feature vectors are passed
as pos_mat
neg_file: The name of the xml file from which feature
vectors for negative samples are to be read.
It may be NULL if feature vectors are passed
as neg_mat*/
void trainSVM(CvMat * pos_mat, CvMat * neg_mat, char *savexml,
char *pos_file = NULL, char *neg_file = NULL)
{
/* Read the feature vectors for positive samples */
if (pos_file != NULL) {
printf("positive loading...\n");
pos_mat = (CvMat *) cvLoad(pos_file);
printf("positive loaded\n");
}
/* Read the feature vectors for negative samples */
if (neg_file != NULL) {
neg_mat = (CvMat *) cvLoad(neg_file);
printf("negative loaded\n");
}
int n_positive, n_negative;
n_positive = pos_mat->rows;
n_negative = neg_mat->rows;
int feature_vector_length = pos_mat->cols;
int total_samples;
total_samples = n_positive + n_negative;
CvMat *trainData = cvCreateMat(total_samples,
feature_vector_length, CV_32FC1);
CvMat *trainClasses = cvCreateMat(total_samples,
1, CV_32FC1);
CvMat trainData1, trainData2, trainClasses1, trainClasses2;
printf("Number of positive Samples : %d\n", pos_mat->rows);
/*Copy the positive feature vectors to training
data*/
cvGetRows(trainData, &trainData1, 0, n_positive);
cvCopy(pos_mat, &trainData1);
cvReleaseMat(&pos_mat);
/*Copy the negative feature vectors to training
data*/
cvGetRows(trainData, &trainData2, n_positive, total_samples);
cvCopy(neg_mat, &trainData2);
cvReleaseMat(&neg_mat);
printf("Number of negative Samples : %d\n", trainData2.rows);
/*Form the training classes for positive and
negative samples. Positive samples belong to class
1 and negative samples belong to class 2 */
cvGetRows(trainClasses, &trainClasses1, 0, n_positive);
cvSet(&trainClasses1, cvScalar(1));
cvGetRows(trainClasses, &trainClasses2, n_positive, total_samples);
cvSet(&trainClasses2, cvScalar(2));
/* Train a linear support vector machine to learn from
the training data. The parameters may played and
experimented with to see their effects*/
CvSVM svm(trainData, trainClasses, 0, 0,
CvSVMParams(CvSVM::C_SVC, CvSVM::LINEAR, 0, 0, 0, 2,
0, 0, 0, cvTermCriteria(CV_TERMCRIT_EPS, 0,
0.01)));
printf("SVM Training Complete!!\n");
/*Save the learnt model*/
if (savexml != NULL) {
svm.save(savexml);
}
cvReleaseMat(&trainClasses);
cvReleaseMat(&trainData);
}

I hope the comments were helpful to understand and use the code. To see how a large collection of files can be renamed to a sequential order which is required by this implementation refer here. Another way to read in the images of dataset could be to store the paths of all files in a text file and parse then parse the text file. I will follow up this post soon, describing how the learnt model can be used for actual detection of an object in an image.

This is follow up post to an earlier post where I have described how an integral histogram can be obtained from an image for fast calculation of hog features. Here I am posting the code for how this integral histogram can be used to calculate the hog feature vectors for an image window. I have commented the code for easier understanding of how it works :

I will very soon post how a support vector machine (svm) can trained using the above functions for an object using a dataset and how the learned model can be used to detect the corresponding object in an image.

Start of borrowed stuffHistograms of Oriented Gradients or HOG features in combination with a support vector machine have been successfully used for object Detection (most popularly pedestrian detection). An Integral Histogram representation can be used for fast calculation of Histograms of Oriented Gradients over arbitrary rectangular regions of the image. The idea of an integral histogram is analogous to that of an integral image, used by viola and jones for fast calculation of haar features for face detection. Mathematically,

where b represents the bin number of the histogram. This way the calculation of hog over any arbitrary rectangle in the image requires just 4*bins number of array references. For more details on integral histogram representation, please refer, [Porikli 2005]. The following demonstrates how such integral histogram can be calculated from an image and used for the calculation of hog features using the opencv computer vision library :

End of borrowed stuff
I will describe how the HOG features for pedestrian detection can be obtained using the above framework and how an svm can be trained for such features for pedestrian detection in a later post.